An image sensing device such as image sensor or any kinds of image processing device works as a front-end unit for capturing image data, and is mainly composed of several semiconductors that are able to record light changes. It converts the optical signals into corresponding electric signals in response to incident light intensities, and further uses an analog-to-digital converter to convert the electric signals into digital signals. The digital signals are often stored in a pixel array form for the subsequent related processing by the image signal processors. However, even though the advanced manufacturing process technology such as complementary metal oxide semiconductor (CMOS) or charge couple device (CCD) is implemented, dead pixels still exist in an image pixel array due to the noise or fabrication errors or bias. In the disclosure, the pixels that are with inappropriate responsibility or unconvertible light intensities in pixels of an image are all referred as dead pixels.
Superficial characteristic of dead pixels, such as a significant difference with their surrounding pixels, results in damage to the visual effect of images. Take the image sensor as an example, to produce a sensor having 100% manufacturing yield rate is a very difficult challenge due to the characteristic of highly complicated process of the image sensor. Even the particle dust in a packaging process may result in losing accuracy of light sensitivity for a single pixel or multiple pixels. Therefore, in fabrication's point of view, it is inevitable for an image sensor that outputs a few dead pixels. Generally, the characteristic of such a dead pixel due to fabrication errors is that the pixel is no longer recovered once it is determined to be a dead pixel, i.e. the position of the dead pixel will not change for different usage times or environments. Based on the characteristic, it is common for the manufacturer to conduct the physical detection for this type of dead pixels (expected dead pixels) before shipment, and record their relevant information in a memory as a correction basis when the sensor is used.
Besides the fabrication process, the severe change of environment temperature may also result in abnormal semiconductor sensitivity and thus produce breaking dead pixels. Moreover, an image sensing device itself is gradually aging due to frequent usage, and appears defects not recorded before factory shipment. The common characteristic of this type of dead pixels (unexpected dead pixels) are all unpredictable and stochastic, so that it is unable to perform compensation based on the dead pixel information recorded in the memory.
For an image sensing device, the so-called dead pixel correction procedure comprises dead pixel detection and dead pixel compensation. Dead pixel detection aims to verify location of possible dead pixels while dead pixel compensation outputs corrected value for each possible dead pixel of known location by using its adjacent good pixels. They are operated separately, but the former must employ before the latter is in use. In existing non-real-time dead pixel correction procedures, the dead pixel detection makes an electric test and/or a luminescent and dim spot test before an image sensing device leaving the factory, and records their relevant dead pixel information. After factory shipment, dead pixel compensation is performed for expected dead pixels based on the known information before the image sensing device outputs each image. While in the real-time dead pixel correction procedures, detection and compensation are performed for the expected/unexpected dead pixels when operating the image sensing device, in order to have the opportunity to increase the usage life cycle and enhance the image quality of the image sensing device.
In some technology-related literatures, for example, one literature disclosed a dead pixel detection technology through a time period to expose. In one embodiment, a controller controls a specific exposure time of a sensor during the startup period of the sensor; then determines whether the obtained light responses are within a predetermined acceptance range, and records the coordinate information of those whose light responses are outside the predetermined acceptance range in a dead pixel distribution map. Upon operating the image sensing device, dead pixel compensation is performed according to the dead pixel distribution map. Another literature disclosed a dead pixel detection technology through a scene change. This technology utilizes the difference value between two image frames, i.e. a previous image frame and a current image frame, and observes whether the difference value complies with a predefined scene change criterion. Once a scene change is sensed, a dead pixel detection mechanism is activated. If a change in the intensity value of a pixel is less than a predefined threshold, it is considered as a dead pixel candidate. Once the number for a pixel being considered as a dead pixel candidate exceeds a predefined threshold value, the pixel is determined as a dead pixel.
Another literature disclosed a technology for combining demosaicing and dead pixel detection. This technology utilizes a median interpolation within a reference range to generate the missing color component of each pixel. If the difference value between a current processing pixel and the pixel within the reference range is greater than a predefined threshold, the current processing pixel is regarded as a dead pixel. Yet one literature disclosed a dead pixel detection technology by considering the pixel-changes within the image and sorting the pixel differences between the current pixel and the neighbor pixels. It is also coupled with the required image quality to detect dead pixels. Yet another literature disclosed a dead pixel detection technology by utilizing predefined defect models and a statistical likelihood to detect dead pixels.
FIG. 1A and FIG. 1B illustrate another real-time dead pixel detection technology. As shown in FIG. 1A, the to-be-detected pixel G5 and its adjacent pixels (G1˜G4 and G6˜G9) are composited to a 3×3 matrix 100. The pixel GH with the second largest value and the pixel GL with the second smallest value are found from the matrix, and a difference value and an average value are computed as follows:Difference value=GH−GL Average value=((G1+G2+G3+G4+G5+G6+G7+G8+G9)−(G5+GH+GL))/6;In the formula of calculating the average value, G5, GH, and GL are not involved in the average computation. In other words, the average value is the average of 6 adjacent pixels within the 3×3 matrix, and these 6 adjacent pixels are the pixels after removing G5, GH, and GL from pixels G1 to G9. As shown in FIG. 1B, if the to-be-detected pixel is in the range of (average value−difference value, average value+difference value), then it is a good pixel; in other words, if the to-be-detected pixel is not within the range, then it is a dead pixel.
In above and other technology associated literatures, some are unable to fully detect unexpected dead pixels appeared after a sensing device leaving the factory while even if others are able to detect unexpected dead pixels, the detection rate is not high for situations of large clustered dead pixels. The multimedia applications of existing electronic products contain a variety of voice, pictures, and video images, and continue to emerge, wherein the information sources of most electronic products are provided by the image sensing device. Keeping accuracy of input data is one of the prerequisites to ensure maximum performance of the algorithms on a variety of applications. Therefore, the real-time dead pixel detection technology of an image sensing device, which may be adapted to an embedded system's computations and still keep a high detection rate, is very worthy of study and development.